Modulation inertial navigation real-time health monitoring method based on observation enhancement

Abstract

The invention belongs to the technical field of inertial navigation, and discloses a modulation inertial navigation real-time health monitoring method based on observation enhancement, which ensures the navigation safety of a carrier in a long-endurance task. According to the method, redundant configured inertial navigation system resources are fully utilized, relative postures, speeds and positions between two sets of single-axis rotation modulation inertial navigation systems are used as constraints for observation, an error model of the inertial navigation system is optimized, and a residual normalized joint error state adaptive state monitoring filter is established; a health state monitoring criterion is established, a threshold value is determined in real time based on estimation output of a filter, meanwhile, an azimuth gyroscopic drift variation fitting method based on the compass effect is designed to achieve separation estimation of azimuth gyroscopic drift, and then real-time monitoring of the health state of the inertial device is achieved. The method does not need the assistance of external information, and provides powerful guarantee for the safety and reliability of long-endurance navigation.

Description

Technical Field

[0001] The present invention belongs to the technical field of inertial navigation, and relates to a real-time health monitoring method for modulated inertial navigation based on observation enhancement, which is applicable to the real-time health monitoring of aviation and marine navigation systems equipped with two or more sets of modulated inertial navigation systems. Background Art

[0002] In the fields of aviation and marine navigation, carriers often need to perform complex and variable tasks. The inertial navigation system can provide real-time navigation information, support the carrier for precise flight or navigation control, and ensure the smooth execution of tasks. It is an indispensable part of aviation and marine navigation. Given that the operating state of the inertial navigation system is related to the safety and stability of the carrier, it is particularly important to build an efficient and sensitive health monitoring and fault diagnosis mechanism.

[0003] In order to ensure that the navigation system has a high degree of reliability and strong fault tolerance, carriers that meet the long-endurance navigation requirements usually redundantly configure multiple sets of inertial navigation systems with a rotation mechanism. However, most of the existing health monitoring methods are limited to evaluating the health status by comparing the consistency of navigation information between inertial navigation systems, or relying on external reference information as an auxiliary means. These methods have certain limitations in practical applications such as insufficient redundancy and underwater environments. At the same time, in scenarios with extremely high real-time requirements, these methods may not be able to quickly and accurately identify and isolate faults, thus affecting the overall performance of the navigation system and the safety of the carrier.

[0004] Aiming at the existing problems, the present invention proposes a real-time health monitoring method for modulated inertial navigation based on observation enhancement for applications lacking external reference information. Taking the relative attitude, velocity, and position between two single-axis rotation modulated inertial navigation systems as constraints as observations, making full use of the resources of the redundantly configured inertial navigation systems, optimizing the error model of the inertial navigation system, and establishing a residual-normalized joint error state adaptive state monitoring filter to realize the real-time tracking and estimation of the zero bias of the horizontal inertial devices and the drift difference of the azimuth gyroscopes of the inertial navigation system. Based on the estimated output of the filter, a real-time state monitoring threshold is determined and a health state monitoring criterion is established to realize the real-time monitoring of the health state of the inertial devices. At the same time, a fitting method for the drift change of the azimuth gyroscope based on the compass effect is designed to realize the separation and estimation of the azimuth gyroscope drift, and then the device-level health monitoring is carried out to meet the rapid identification and response of navigation system faults, ensuring the navigation safety of the carrier during long-endurance tasks. Summary of the Invention

[0005] The present invention proposes a real-time health monitoring method for modulated inertial navigation based on observation enhancement, which can realize device-level health monitoring of redundant single-axis rotation modulated inertial navigation systems.

[0006] To solve the above technical problems, the solution proposed by the present invention is as follows:

[0007] A real-time health monitoring method for modulated inertial navigation based on observation enhancement, the method comprising the following steps:

[0008] (1) Two sets of modulated inertial navigation systems operating in the navigation state rotate and modulate around their respective azimuth axes according to a set modulation strategy, and output navigation information; define the two sets of single-axis rotation modulation inertial navigation systems as inertial navigation 1 and inertial navigation 2 respectively, and define the body coordinate systems of the two sets of inertial navigation. Among them, the body coordinate system b1 of inertial navigation 1 and the body coordinate system b2 of inertial navigation 2 are both defined as "right-front-up";

[0009] (2) Establish a dynamic model based on geometric constraint observation by using the navigation output information of the two sets of single-axis rotation modulation inertial navigation systems. The specific steps are as follows:

[0010] (2.1) Determine the joint error equation. By taking the difference between the error equations of the two sets of inertial navigation, the joint error equation of the system is obtained:

[0011]

[0012] Among them,

[0013]

[0014] δL 12 =δL1 - δL2, δλ 12 =δλ1 - δλ2

[0015]

[0016] In the formula, (·) 12 represents the difference between the corresponding error states of inertial navigation 1 and inertial navigation 2, represents the attitude error φ1 of inertial navigation 1 n and the attitude error φ2 of inertial navigation 2 n The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geodetic system. and respectively represent the direction cosine matrices from the body coordinate system b1 of inertial navigation 1 and the body coordinate system b2 of inertial navigation 2 to the navigation coordinate system n. represents the velocity error of inertial navigation 1 after error correction and the velocity error of inertial navigation 2 The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geodetic system. δL 12 represents the difference between the latitude error δL1 of inertial navigation 1 and the latitude error δL2 of inertial navigation 2, δλ 12 represents the difference between the longitude error δλ1 of inertial navigation 1 and the longitude error δλ2 of inertial navigation 2, v n =[v E vN v U T represents the true velocity of the vehicle, and the subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geographical coordinate system. respectively represent the velocity output values of INS 1 and INS 2, L and h respectively represent the true latitude and altitude of the vehicle, and R E and R N are respectively the radius of the prime vertical circle and the radius of the meridian circle at the location of the vehicle; is the angular velocity vector of the Earth's rotation, is the angular velocity of rotation of the navigation coordinate system relative to the Earth coordinate system, is the angular velocity of rotation of the navigation coordinate system relative to the inertial coordinate system, represents the difference in the angular velocity error of the Earth's rotation related to the latitude errors of the two sets of INSs respectively, represents the difference in the transfer angular velocity error related to the latitude errors and velocity errors of the two sets of INSs respectively, represents the gyro assembly error of INS m, where m is the number of INS 1 and INS 2, is modeled as a constant drift and gyro noise sum, where, represents the x-axis gyro drift of INS m, represents the y-axis gyro drift of INS m, represents the z-axis gyro drift of INS m, respectively represent the x-axis, y-axis, and z-axis gyro noises of INS m, represents the accelerometer assembly error of INS m, which is modeled as a constant zero bias and accelerometer noise sum, where, represents the x-axis accelerometer zero bias of INS m, represents the y-axis accelerometer zero bias of INS m, represents the z-axis accelerometer zero bias of INS m, respectively represent the x-axis, y-axis, and z-axis accelerometer noises of INS m;

[0017] (2.2) Determine the joint state equation:

[0018]

[0019] where,

[0020]

[0021] In the formula, X represents the joint error state vector, w represents the process noise vector, and the system state matrix F and the process noise matrix G are determined by the joint error equation.​

[0022] (2.3) Determine the state constraint observation equation;

[0023] Considering the influence of the lever arm, the velocity and position outputs of INS1 and INS2 are respectively expressed as:

[0024]

[0025] Where,

[0026]

[0027] δp1 = [δL1 δλ1 δh1] T , δp2 = [δL2 δλ2 δh2] T

[0028]

[0029] In the formula, p = [L λ h] T represents the true position of the carrier, λ represents the true longitude of the carrier, is the position output of INS1, and δp1 are respectively the velocity error and position error of INS1 before error correction, and δh1 represents the height error of INS1, is the position output of INS2, and δp2 are respectively the velocity error and position error of INS2 before error correction, and δh2 represents the height error of INS2, represents the direction cosine matrix from the body coordinate system b to the navigation coordinate system n, represents the angular rate of the body coordinate system b relative to the earth coordinate system e, and are respectively the lever arms between the centers of INS1 and INS2 and the center of the carrier;

[0030] Define the body coordinate systems of INS1 and INS2 at the initial moment as b 10 system and b 20 system. According to the output of the high-precision optoelectronic encoder and the attitude matrices output by the two sets of single-axis rotating modulation INSs respectively, the difference relationship of the attitude errors of the two sets of single-axis rotating INSs can be determined, expressed as:

[0031]

[0032] In the formula, and respectively represent the attitude matrices output by INS1 and INS2, and respectively represent the indexing mechanism zero position coordinate systems b 10 system and b from the body coordinate system b to INS1 and INS220 The direction cosine matrix of the system and respectively represent the attitude matrices of the zero-position coordinate systems of the indexing mechanisms of INS 1 and INS 2 with respect to the current moments of each INS, which are determined by the outputs of the optoelectronic encoders;

[0033] After the system is installed, the lever arm is accurately calibrated. The relative attitude between the two single-axis rotation modulation INSs and the differences between the velocity outputs and position outputs are used as the observation outputs of the observation-enhanced dynamic model. The observation equation is expressed as:

[0034] Z = HX + ν

[0035] where

[0036]

[0037] In the formula, Z represents the observation vector, H represents the observation matrix, and ν represents the observation noise vector; and represent the differences in the eastward and northward velocity errors of the two sets of INSs before error correction; correspondingly, the observation matrix is expressed as:

[0038]

[0039] In the formula, I m represents the m×m identity matrix;

[0040] (3) Establish an adaptive state monitoring filter with residual normalization;

[0041] Introduce a time-varying fading factor λ k / k-1 into the one-step prediction error covariance P k to construct a strong tracking filter, which is expressed as:

[0042]

[0043] In the formula, Φ k / k-1 represents the one-step transition matrix after discretization, P k-1 represents the mean square error matrix of state estimation, Γ k-1 represents the noise distribution matrix, and Q k-1 represents the covariance matrix of the process noise w k-1 ; determine the time-varying fading factor λ k as follows:

[0044]

[0045] where

[0046]

[0047] where Tr(·) represents the rank of a matrix, and H k represents the discretized observation matrix, R k represents the covariance matrix of the observation noise, l k represents the weakening factor, which is selected based on experience or obtained through computer simulation, represents the innovation variance at time k, represents the innovation variance at time k-1, is the system residual at time 0, is the system residual at time k, ρ represents the forgetting factor, taken as 0.95 ≤ ρ ≤ 0.995, η = diag(η1, η2, …, η7) is a matrix used to achieve residual normalization, and the implementation is as follows:

[0048]

[0049] where Z0 = [Z0(1) Z0(2) Z0(3) Z0(4) Z0(5) Z0(6) Z0(7)] T is a set of output values obtained through prior knowledge and used to replace each component in;

[0050] (4) Real-time health status monitoring, and the specific steps are as follows:

[0051] (4.1) Calculate real-time status monitoring parameters:

[0052] Determine the real-time status detection threshold by analyzing the statistical characteristics of the output of the monitoring filter, and then realize the real-time health status monitoring of the inertial device. The determination of the real-time status monitoring threshold and the judgment criterion are as follows:

[0053] Step 1: Set a preselected data window that slides with time, with a length of N. The filter output information included in this sliding window at time k is Calculate the data statistical characteristics as follows:

[0054]

[0055] where μ k and respectively represent the mean and variance of the real-time estimation parameters of the filter within the sliding window at time k, and the dimensions of both are the same as the dimension;

[0056] Step 2: Set the weighting coefficient α and perform iterative calculation on the mean of the historical sliding window:

[0057] Σ k = α·μ k +(1 - α)Σ k-1

[0058] Where, Σ k represents the sliding window mean value after iteration at time k. Further, the following high and low thresholds are determined as follows:

[0059] T - = Σ k + k1σ k

[0060] T + = k2Σ k

[0061] Where, k1 and k2 are parameters to be adjusted, and k1 ≥ 1, k2 > 1, taking integer values; T + is the high threshold, and T - is the low threshold;

[0062] Step 3: Establish the health state monitoring criterion as:

[0063]

[0064] Where, represents the i-th component of the filter estimated output at time k + 1, and T + (i) and T - (i) respectively represent the i-th components of the real-time high threshold and low threshold to be obtained;

[0065] At this time, the health state monitoring of the horizontal inertial device and the abnormal state detection of the azimuth gyro can be realized. If an abnormal state of the azimuth gyro is found, enter (4.2);

[0066] (4.2) Azimuth gyro abnormal state monitoring:

[0067] Within several hours of the abnormal state of the azimuth gyro, the influence of the Foucault oscillation and the 24-hour oscillation can be ignored, and the error propagation characteristics in the north channel, east channel, and vertical channel can be considered independently. Analyze the influence of the abnormal state of the azimuth gyro in the north channel:

[0068]

[0069] Where, τ * represents the time when the abnormality is detected, t represents the time after the abnormality is detected, and △v N (t) = δv N (t) - δv N (τ * ) represents the northward velocity increment, represents the change in the azimuth gyro drift, and ω sDenote the Schuler frequency. After damping the northward velocity increment using two sets of inertial navigation systems, the drift change of each azimuth gyro is fitted respectively, and the abnormal state diagnosis of the azimuth gyro is realized through comparison.

[0070] Further, in step (1), both inertial navigation 1 and inertial navigation 2 perform four-position rotation-stop modulation around their respective azimuth axes, and the rotation order is reverse.

[0071] Further, in step (1), inertial navigation 1 and inertial navigation 2 rotate at different times according to the same rotation order, that is, the two inertial navigations rotate in an asynchronous manner according to the same rotation scheme.

[0072] Further, in step (2), the lever arm between inertial navigation 1 and inertial navigation 2 is calibrated and determined after the two sets of inertial navigations are installed.

[0073] Further, in step (2) and is determined by the angular position of the rotating frame output by the indexing mechanism.

[0074] Further, in step (3), the covariance matrix R of the observation noise k is selected according to the filtering innovation value when there is no fault.

[0075] Further, the method of the present invention is not only applicable to the case where both inertial navigation 1 and inertial navigation 2 are single-axis rotation modulation inertial navigations, but also applicable to the cases where inertial navigation 1 and inertial navigation 2 are two-axis rotation modulation inertial navigations or three-axis rotation modulation inertial navigations.

[0076] Compared with the prior art, the present invention has the following advantages:

[0077] There is no need to perform specific configuration on the inertial navigation system, and the health status monitoring of inertial devices is realized by jointly using the output information of the two sets of inertial navigation systems. At the same time, the azimuth gyro drift increment estimation method solves the problem of abnormal state monitoring of the azimuth gyro and realizes device-level health monitoring. The present invention does not require accurate reference information assistance, providing a reliable guarantee for the safety and reliability of long-term navigation in a GNSS-denied environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is a flowchart of the method provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0079] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0080] As Figure 1As shown in the figure, a real-time health monitoring method for modulation inertial navigation based on observation enhancement is as follows:

[0081] (1) Two sets of modulation inertial navigation systems operating in the navigation state rotate and modulate around their respective azimuth axes according to the set modulation strategy, and output navigation information; define the two sets of single-axis rotation modulation inertial navigation systems as inertial navigation 1 and inertial navigation 2 respectively, and define the body coordinate systems of the two sets of inertial navigation. Among them, the body coordinate system b1 of inertial navigation 1 and the body coordinate system b2 of inertial navigation 2 are both defined as "right-front-up";

[0082] (2) Establish a dynamic model based on geometric constraint observation using the navigation output information of the two sets of single-axis rotation modulation inertial navigation systems. The specific steps are as follows:

[0083] (2.1) Determine the combined error equation. By subtracting the error equations of the two sets of inertial navigation, the combined error equation of the system is obtained:

[0084]

[0085] Among them,

[0086]

[0087] δL 12 =δL1 - δL2, δλ 12 =δλ1 - δλ2

[0088]

[0089] In the formula, (·) 12 represents the difference between the corresponding error states of inertial navigation 1 and inertial navigation 2, represents the attitude error φ1 of inertial navigation 1 n and the attitude error φ2 of inertial navigation 2 n The difference between them. The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geodetic coordinate system. and respectively represent the direction cosine matrices from the body coordinate system b1 of inertial navigation 1 and the body coordinate system b2 of inertial navigation 2 to the navigation coordinate system n. represents the velocity error of inertial navigation 1 after error correction and the velocity error of inertial navigation 2 The difference between them. The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geodetic coordinate system. δL 12 represents the difference between the latitude error δL1 of inertial navigation 1 and the latitude error δL2 of inertial navigation 2, and δλ 12 represents the difference between the longitude error δλ1 of inertial navigation 1 and the longitude error δλ2 of inertial navigation 2, v n =[v E v Nv U T represents the true velocity of the vehicle. The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geographical coordinate system. L and h respectively represent the true latitude and altitude of the vehicle, and R E and R N are respectively the radius of the prime vertical circle and the radius of the meridian circle at the location of the vehicle; is the angular velocity vector of the Earth's rotation, is the angular velocity of rotation of the navigation coordinate system relative to the Earth coordinate system, is the angular velocity of rotation of the navigation coordinate system relative to the inertial coordinate system, represents the difference in the angular velocity error of the Earth's rotation related to the latitude errors of the two inertial navigation systems respectively, represents the difference in the transfer angular velocity error related to the latitude errors and velocity errors of the two inertial navigation systems respectively, represents the gyro component error of inertial navigation m, where m is the number of inertial navigation 1 and inertial navigation 2, is modeled as a constant drift and gyro noise The sum of, where, represents the gyro drift of the x-axis of inertial navigation m, represents the gyro drift of the y-axis of inertial navigation m, represents the gyro drift of the z-axis of inertial navigation m, respectively represent the gyro noise of the x-axis, y-axis, and z-axis of inertial navigation m, represents the accelerometer component error of inertial navigation m, which is modeled as a constant zero bias and accelerometer noise The sum of, where, represents the zero bias of the x-axis accelerometer of inertial navigation m, represents the zero bias of the y-axis accelerometer of inertial navigation m, represents the zero bias of the z-axis accelerometer of inertial navigation m, respectively represent the accelerometer noise of the x-axis, y-axis, and z-axis of inertial navigation m;

[0090] (2.2) Determine the joint state equation:

[0091]

[0092] Among them,

[0093]

[0094] In the formula, X represents the joint error state vector, w represents the process noise vector, and the system state matrix F and the process noise matrix G are determined by the joint error equation;

[0095] ​(2.3) Determine the state constraint observation equation;

[0096] Considering the influence of lever arm, the velocity and position outputs of INS1 and INS2 are respectively expressed as:

[0097]

[0098] Where,

[0099]

[0100] δp1 = [δL1 δλ1 δh1] T , δp2 = [δL2 δλ2 δh2] T

[0101]

[0102] In the formula, p = [L λ h] T represents the true velocity of the carrier, λ represents the true longitude of the carrier, and are respectively the velocity output and position output of INS1, and δp1 are respectively the velocity error and position error of INS1 before error correction, and δh1 represents the height error of INS1, and are respectively the velocity output and position output of INS2, and δp2 are respectively the velocity error and position error of INS2 before error correction, and δh2 represents the height error of INS2, represents the direction cosine matrix from the body coordinate system b to the navigation coordinate system n of the carrier, represents the angular rate of the body coordinate system b of the carrier relative to the earth coordinate system e, and are respectively the lever arms between the centers of INS1 and INS2 and the center of the carrier;

[0103] Define the body coordinate systems of INS1 and INS2 at the initial moment as the b 10 system and the b 20 system. According to the output of the high-precision optoelectronic encoder and the attitude matrices output by the two sets of single-axis rotation modulation INSs respectively, the difference relationship of the attitude errors of the two sets of single-axis rotation INSs can be determined, which is expressed as:

[0104]

[0105] In the formula, and respectively represent the attitude matrices output by INS1 and INS2, and respectively represent the direction cosine matrices from the carrier coordinate system b to the zero-position coordinate systems of the indexing mechanisms of INS1 and INS2, and respectively represent the attitude matrices of the zero-position coordinate systems of the indexing mechanisms of INS1 and INS2 relative to the current moment, which are determined by the outputs of the optoelectronic encoders;

[0106] After the system is installed, the lever arm is accurately calibrated. The differences between the relative attitudes, velocity outputs, and position outputs of the two single-axis rotation modulation INSs are used as the observation outputs of the observation-enhanced dynamic model. The observation equation is expressed as:

[0107] Z = HX + ν

[0108] where,

[0109]

[0110] In the formula, Z represents the observation vector, H represents the observation matrix, and ν represents the observation noise vector, and represent the differences in the eastward and northward velocity errors of the two sets of INSs before error correction; correspondingly, the observation matrix is expressed as:

[0111]

[0112] In the formula, I m represents the m×m identity matrix;

[0113] (3) Establish an adaptive state monitoring filter with residual normalization;

[0114] Introduce a time-varying fading factor λ k / k-1 into the one-step prediction error covariance P k to construct a strong tracking filter, which is expressed as:

[0115]

[0116] In the formula, Φ k / k-1 represents the discretized one-step transition matrix, P k-1 represents the mean square error matrix of state estimation, Γ k-1 represents the noise distribution matrix, and Q k-1 represents the covariance matrix of the process noise w k-1 ; determine the time-varying fading factor λ k as follows:

[0117] First, introduce a diagonal matrix η = diag(η1, η2,..., η n ) for the system

[0118] where,

[0119]

[0120] Among them,

[0121]

[0122] In the formula, Tr(·) represents taking the rank of a matrix, and H k represents the discretized observation matrix, R k represents the covariance matrix of the observation noise, l k represents the weakening factor, which is selected according to experience or obtained through computer simulation. represents the innovation variance at time k, represents the innovation variance at time k - 1, is the system residual at time 0, is the system residual at time k, ρ represents the forgetting factor, and is taken as 0.95 ≤ ρ ≤ 0.995. η = diag(η1, η2,..., η7) is a matrix used to achieve residual normalization, and the implementation is as follows:

[0123]

[0124] In the formula, Z0 = [Z0(1) Z0(2) Z0(3) Z0(4) Z0(5) Z0(6) Z0(7)] T is a set of system output values obtained through prior knowledge and used to replace ;

[0125] (4) Real-time health status monitoring, and the specific steps are as follows:

[0126] (4.1) Calculate real-time status monitoring parameters:

[0127] Determine the real-time status detection threshold by analyzing the statistical characteristics of the output of the monitoring filter, and then realize the real-time health status monitoring of inertial devices. The real-time status monitoring threshold and judgment criterion are as follows:

[0128] Step 1: Set a preselected data window that slides with time, with a length of N. The filter output information included in this sliding window at time k is Calculate the data statistical characteristics as follows:

[0129]

[0130] In the formula, μ k and respectively represent the mean and variance of the real-time estimation parameters of the filter within the sliding window at time k, and the dimensions of both are the same as those of ;

[0131] Step 2: Set the weighting coefficient α and perform iterative calculation on the mean value of the historical sliding window:

[0132] Σ k = α·μ k +(1 - α)Σ k-1

[0133] In the formula, Σ k represents the sliding window mean value after iteration at time k. Further determine the high and low thresholds as follows:

[0134] T - = Σ k + k1σ k

[0135] T + = k2Σ k

[0136] In the formula, k1 and k2 are parameters to be adjusted, and k1≥1, k2>1, taking integer values; T + is the high threshold, and T - is the low threshold;

[0137] Step 3: Establish the health status monitoring criterion as:

[0138]

[0139] In the formula, represents the i-th component of the filter estimated output at time k + 1, and T + (i) and T - (i) represent the i-th components of the real-time high threshold and low threshold to be obtained respectively;

[0140] At this time, the health status monitoring of the horizontal inertial device and the abnormal status detection of the azimuth gyro can be realized. If an abnormal status of the azimuth gyro is found, enter (4.2);

[0141] (4.2) Azimuth gyro abnormal status monitoring:

[0142] Within several hours of the azimuth gyro failure, the effects of the Foucault oscillation and the 24-hour oscillation can be ignored, and the error propagation characteristics in the north channel, east channel, and vertical channel can be considered independently; analyze the influence of the azimuth gyro abnormal status in the north channel:

[0143]

[0144] In the formula, τ * represents the time when the abnormality is detected, t represents the time after the abnormality is detected, and △v N (t) = δv N (t)-δv N (τ* ) represents the northward velocity increment, represents the change in azimuth gyro drift, ω s represents the Schuler frequency. The change in azimuth gyro drift is respectively fitted by using the northward velocity increment after damping of two inertial navigation systems, and the abnormal state diagnosis of the azimuth gyro is realized through comparison.

[0145] Further, in the step (1), both inertial navigation 1 and inertial navigation 2 perform four-position rotation and stop modulation around their respective azimuth axes, and the rotation order is reverse.

[0146] Further, in the step (1), inertial navigation 1 and inertial navigation 2 rotate at different times according to the same rotation order, that is, the two inertial navigations rotate in an asynchronous manner according to the same rotation scheme.

[0147] Further, the lever arm between inertial navigation 1 and inertial navigation 2 in the step (2) is determined after calibration when the two inertial navigations are installed.

[0148] Further, in the step (2) and is determined by the angular position of the rotating frame output by the indexing mechanism.

[0149] Further, the covariance matrix R of the observation noise in the step (3) k is selected according to the filtering innovation value when there is no fault.

[0150] Further, the method of the present invention is not only applicable to the case where both inertial navigation 1 and inertial navigation 2 are single-axis rotation modulation inertial navigations, but also applicable to the cases where inertial navigation 1 and inertial navigation 2 are two-axis rotation modulation inertial navigations or three-axis rotation modulation inertial navigations.

[0151] The following further illustrates the present invention with experiments:

[0152] Experiments are carried out using two laser gyro inertial navigation systems. Among them, the gyro drift stability is better than 0.003° / h, and the accelerometer zero stability is better than 20 μg. After initial alignment, both inertial navigations work in the autonomous navigation state, and the original data is collected and processed offline. To verify the effectiveness of the present invention, abnormal states are artificially designed to occur in the gyro or accelerometer of inertial navigation 1 or inertial navigation 2, and then the algorithm proposed in this paper is used to perform real-time health monitoring on it. Repeated experiments show that the diagnostic results are consistent with the design results, verifying that the method proposed in the present invention can achieve reliable real-time health monitoring.

[0153] The above is only the preferred embodiment of the present invention, and is not intended to limit the present invention. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. Several improvements and retouches made without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A real-time health monitoring method for modulation inertial navigation based on observation enhancement, characterized in that Including the following steps: (1) Two sets of modulation inertial navigation systems operating in the navigation state rotate and modulate around their respective azimuth axes according to the set modulation strategy, and output navigation information; define two sets of single-axis rotation modulation inertial navigation systems as inertial navigation 1 and inertial navigation 2 respectively, and define the body coordinate systems of the two sets of inertial navigation. Among them, the body coordinate system b1 of inertial navigation 1 and the body coordinate system b2 of inertial navigation 2 are both defined as "right-front-up"; (2) Establish a dynamic model based on geometric constraint observation using the navigation output information of the two sets of single-axis rotation modulation inertial navigation systems. The specific steps are as follows: (2.1) Determine the combined error equation. By subtracting the error equations of the two sets of inertial navigation, obtain the combined error equation of the system: Where, δL 12 = δL1 - δL2, δλ 12 = δλ1 - δλ2 where, (·) 12 denotes the difference between the corresponding error states of INS1 and INS2, denotes the attitude error of INS1 and the attitude error of INS2 The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geographical coordinate system. and respectively denote the direction cosine matrices from the body coordinate system b1 of INS1 and the body coordinate system b2 of INS2 to the navigation coordinate system n. denotes the velocity error of INS1 after error correction and the velocity error of INS2 The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geographical coordinate system. δL 12 denotes the difference between the latitude error δL1 of INS1 and the latitude error δL2 of INS2, δλ 12 denotes the difference between the longitude error δλ1 of INS1 and the longitude error δλ2 of INS2, v n = [v E v N v U T denotes the true velocity of the carrier. The subscripts E, N, and U respectively represent the components of the corresponding error quantities along the east, north, and up directions of the geographical coordinate system. respectively denote the velocity output values of INS1 and INS2. L and h respectively represent the true latitude and altitude of the carrier. R E and R N are respectively the radius of the prime vertical circle and the radius of the meridian circle at the location of the carrier; is the angular velocity vector of the Earth's rotation, is the angular velocity of the rotation of the navigation coordinate system relative to the Earth coordinate system, is the angular velocity of the rotation of the navigation coordinate system relative to the inertial coordinate system, denotes the difference between the angular velocity errors of the Earth's rotation respectively related to the latitude errors of the two sets of INSs, denotes the difference between the transfer angular velocity errors respectively related to the latitude errors and velocity errors of the two sets of INSs, denotes the gyro assembly error of INS m, where m is the number of INS1 or INS2, is modeled as the sum of a constant drift and gyro noise where, denotes the gyro drift of the x-axis of INS m, denotes the gyro drift of the y-axis of INS m, denotes the gyro drift of the z-axis of INS m, respectively denote the gyro noise of the x-axis, y-axis, and z-axis of INS m,​ The accelerometer component error of the inertial navigation m is modeled as a constant bias and accelerometer noise The sum of where, represents the x-axis accelerometer bias of the inertial navigation m, represents the y-axis accelerometer bias of the inertial navigation m, represents the z-axis accelerometer bias of the inertial navigation m, respectively represent the x-axis, y-axis, and z-axis accelerometer noises of the inertial navigation m; (2.2) Determine the combined state equation: Where, In the formula, X represents the combined error state vector, w represents the process noise vector, and the system state matrix F and the process noise matrix G are determined by the combined error equation; (2.3) Determine the state constraint observation equation; Considering the influence of the lever arm, the velocity and position outputs of inertial navigation 1 and inertial navigation 2 are respectively expressed as: Where, δp1 = [δL1 δλ1 δh1] T where δp2 = [δL2 δλ2 δh2] T where p = [Lλh] T represents the true position of the vehicle, λ represents the true longitude of the vehicle, is the position output of INS 1, and δp1 are the velocity error and position error of INS 1 before error correction respectively, and δh1 represents the height error of INS 1, is the position output of INS 2, and δp2 are the velocity error and position error of INS 2 before error correction respectively, and δh2 represents the height error of INS 2, represents the direction cosine matrix from the vehicle coordinate system b to the navigation coordinate system n, represents the angular rate of the vehicle coordinate system b relative to the earth coordinate system e, and are the lever arms between the centers of INS 1 and INS 2 and the vehicle center respectively; Define the body coordinate systems of INS1 and INS2 at the initial moment as the b 10 system and the b 20 system. According to the output of the high-precision optical encoder and the attitude matrices output by the two sets of single-axis rotation modulation INSs respectively, the difference relationship of the attitude errors of the two sets of single-axis rotation INSs can be determined, expressed as: In the formula, and respectively represent the attitude matrices output by inertial navigation 1 and inertial navigation 2, and respectively represent the direction cosine matrices from the carrier coordinate system b to the zero-position coordinate systems b 10 system and b 20 system of the indexing mechanisms of inertial navigation 1 and inertial navigation 2, and respectively represent the attitude matrices of the zero-position coordinate systems of the indexing mechanisms of inertial navigation 1 and inertial navigation 2 relative to the current moments of the respective inertial navigations, which are determined by the outputs of the optoelectronic encoders; Precisely calibrate the lever arm after system installation Take the difference between the relative attitude, speed output, and position output of the two single-axis rotation modulation inertial navigation systems as the observation output of the observation-enhanced dynamic model, and express the observation equation as: Z = HX + ν Where, where \(Z\) represents the observation vector, \(H\) represents the observation matrix, and \(\nu\) represents the observation noise vector. and represent the differences in the eastward and northward velocity errors of the two sets of inertial navigation before error correction; correspondingly, the observation matrix is expressed as: where I m represents an m-by-m identity matrix; (3) Establish an adaptive state monitoring filter with residual normalization; Introduce a time-varying fading factor λ k / k-1 into the one-step prediction error covariance P k to construct a strong tracking filter, expressed as: where Φ k / k-1 represents the one-step transition matrix after discretization, P k-1 represents the mean square error matrix of state estimation, Γ k-1 represents the noise distribution matrix, Q k-1 represents the covariance matrix of the process noise w k-1 ; the time-varying fading factor λ k is determined as follows: Where, where Tr(·) represents the rank of a matrix, H k represents the discretized observation matrix, R k represents the covariance matrix of the observation noise, l k represents the weakening factor, which is selected according to experience or obtained through computer simulation, represents the innovation variance at time k, represents the innovation variance at time k - 1, is the system residual at time 0, is the system residual at time k, ρ represents the forgetting factor, taken as 0.95 ≤ ρ ≤ 0.995, η = diag(η1, η2, …, η7) is the matrix used to achieve residual normalization, and the implementation is as follows: where \(Z_0 = [Z_0(1)Z_0(2)Z_0(3)Z_0(4)Z_0(5)Z_0(6)Z_0(7)]\) T is a set of output values obtained through prior knowledge for substituting each component in (4) Real-time health state monitoring. The specific steps are as follows: (4.1) Calculate the real-time state monitoring threshold: Determine the real-time state detection threshold by analyzing the statistical characteristics of the output of the monitoring filter, and then realize the real-time health state monitoring of inertial devices. The determination of the real-time state monitoring threshold and the judgment criterion are as follows: Step 1: Set a preselected data window that slides over time, with a length of N. The filter output information contained in this sliding window at time k is Calculate the data statistical characteristics as follows: where, μ k and represent the mean and variance of the real-time estimated parameters of the filter within the sliding window at time k, respectively, and the dimensions of both are the same as those of the dimension; Step 2: Set the weighting coefficient α and perform iterative calculation on the mean value of the historical sliding window; Σ k = α·μ k + (1 - α)Σ k-1 where, Σ k represents the moving window mean value after iteration at the k-th moment, and the upper and lower thresholds are further determined as follows: T - = Σ k + k1σ k T + = k2Σ k where k1 and k2 are parameters to be adjusted, and k1≥1, k2>1, both being integers; T + is the high threshold, and T - is the low threshold; Step 3: Establish the health state monitoring criterion as: wherein, represents the i-th component of the filter estimated output at the (k + 1)-th moment, T + (i) and T - (i) respectively represent the i-th components of the required real-time high threshold and low threshold; At this time, the health state monitoring of horizontal inertial devices and the abnormal state detection of azimuth gyroscopes can be realized. If an abnormal state of the azimuth gyroscope is found, enter (4.2); (4.2) Abnormal state monitoring of the azimuth gyroscope: Within several hours when the state of the azimuth gyroscope is abnormal, the influence of the Foucault oscillation and the 24-hour oscillation can be ignored, and the error propagation characteristics in the north channel, east channel, and vertical channel can be considered independently; analyze the influence of the abnormal state of the azimuth gyroscope in the north channel: where τ * represents the time when an anomaly is detected, t represents the time after the anomaly is detected, and Δv N (t) = δv N (t) - δv N (τ * ) represents the northward velocity increment, represents the change in the azimuth gyro drift, ω s represents the Schuler frequency. The change in the azimuth gyro drift is respectively fitted by using the northward velocity increment after damping of two sets of inertial navigation systems, and the diagnosis of the abnormal state of the azimuth gyro is realized through comparison.

2. The method for real-time health monitoring of a modulation inertial navigation based on observation enhancement according to claim 1, wherein In step (1), both inertial navigation 1 and inertial navigation 2 perform four-position rotation and stop modulation around their respective azimuth axes, and the rotation order is reversed.

3. The real-time health monitoring method for modulation inertial navigation based on observation enhancement as claimed in claim 1, wherein In step (1), inertial navigation 1 and inertial navigation 2 rotate at different times according to the same rotation order, that is, the two inertial navigations rotate in an asynchronous manner according to the same rotation scheme.

4. The real-time health monitoring method for modulation inertial navigation based on observation enhancement according to claim 1, characterized in that In step (2), the lever arm between inertial navigation 1 and inertial navigation 2 is calibrated and determined after the installation of the two sets of inertial navigation.

5. The method for real-time health monitoring of a modulation inertial navigation based on observation enhancement according to claim 1, wherein In the said step (2) and determine the angular position of the rotating frame output by the indexing mechanism.

6. The real-time health monitoring method for modulation inertial navigation based on observation enhancement according to claim 1, characterized in that The covariance matrix R of the observation noise in the step (3) k Selected according to the filtering innovation value when there is no fault.

7. The method for real-time health monitoring of a modulation inertial navigation based on observation enhancement according to claim 1, wherein The method of the present invention is not only applicable to the case where both inertial navigation 1 and inertial navigation 2 are single-axis rotation modulation inertial navigation, but also applicable to the case where inertial navigation 1 and inertial navigation 2 are two-axis rotation modulation inertial navigation or three-axis rotation modulation inertial navigation.

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